1 / 17

V V Zharkova, S S Ipson

Survey of feature recognition techniques Work package 5 Bradford University & Meudon Observatory. V V Zharkova, S S Ipson. Summary of the recognition techniques. P - pre-processing , I - user interaction was required and A - automated method.

atalo
Télécharger la présentation

V V Zharkova, S S Ipson

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Survey of feature recognition techniquesWork package 5 Bradford University & Meudon Observatory V V Zharkova, S S Ipson

  2. Summary of the recognition techniques SFR Workshop 1, BRO, Brussels, 23-24 Oct '03 P - pre-processing, I - user interaction was requiredand A - automated method

  3. II. Survey of Pattern Recognition Techniques • 3.1 Image preparation • 3.1.1 Geometrical distortion • 3.1.2 Blurring • 3.1.3 Intensity calibration • 3.1.4 Miscellaneous defects • 3.2 Detection of sunspots • 3.2.1 Histogram methods • 3.2.2 LOG methods • 3.2.3 Region growing methods • 3.2.4 Simulated annealing • 3.3 Filament detection • 3.3.1 Chain linking procedure • 3.3.2 Region growing procedure • 3.4 Detection of active regions (plage) • 3.4.1 Global intensity threshold • 3.4.2 Region growing methods • 3.4.3 Bayesian inference method • 3.5 Detection of coronal mass ejections • 3.5.1 Hough transform method • 3.5.2 Multiple abstraction level mining • method • 2.1 Histogram-based segmentation • 2.2. Region-based segmentation • 2.2.1 Region growing • 2.2.2 Clustering • 2.2.3 Multi-resolution transforms • 2.3 Edge-based segmentation • 2.3.1 Gradient operator based edge detection • 2.3.2 Canny edge detection • 2.3.3 Laplacian of Gaussian zero-crossing edge detection • 2.4 Artificial neural networks • 2.4.1 Standard technique • 2.4.2 Cascade-correlation architecture • 2.4.3 Evolving cascade neural networks • 2.4.4 GMDH-type neural networks • 2.4.5 Generalized regression neural networks • 2.5 Explicit-model based segmentation • 2.5.1 The Hough transform • 2.5.2 Ribbon detection • 2.6 Models based on functionals • 2.6.1 Active contours • 2.7 Bayesian inference • 2.8 Motion segmentation • 2.9 Shape analysis • 2.10 Classification SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  4. III.a. Why the pre-processing techniques? Difficulties with images: • Errors in FITS header information • Image shape (ellipse), centre and the pole coordinates • Weather transparency (clouds) and different thickness of atmosphere • Centre-to-limb darkening • Defects in data (strips, lines, intensity) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  5. SUNSPOTSSynoptic Charts Central Meridian Synoptic Map SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  6. Image segmentation procedures • Thresholding approaches (histogram-based segmentation) • Edge-based methods (using the first or second derivatives of the spatio-temporal functions • Region growing methods (intitial starting pixel + criterion for merging) • Hybrid region growing and edge detection techniques • Neural networks (training without explicit criteria) • Global Information methods (Bayesian, functional models, Hough transform) • Miscellaneous (data clustering, simulated annealing, data mining) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  7. General techniques • Histogram-based segmentation – • Analyse the grey-level histograms • Size of the segmented object varies with the threshold • Give good results on a uniform background • Objects had a distinct intensity range • Region-based segmentation • Region growing (start from seeds and grow regions on specified criteria) • Clustering (pixels are clustered in a feature space using any discriminating feature asociated and then connecting regions are found) • Edge-based segmentation • Relies on discontinuities in the image data to locate boundaries • But edge profile is not known • Profile can vary with edge (shading or texture) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  8. Edge-based segmentation • Gradient operator based edge detection – • Vertical and horizontal components are finite difference formulae with • Sobel convolution masks: vertical and horizontal -1 -2 -1 -1 0 1 0 0 0 -2 0 2 1 2 1 -1 0 1 • Gradient magnitude - a square root of the sum of the square gradient components • Candidate edge located with gradient magnitude above threshold • Multi passes of the detected edge • Canny edge detection • Smooth image with a Gaussian filter • Compute gradient magnitude and orientation with finite differences • Apply non-maxima suppression to thin the gradient-magnitude edge image • Track along edges starting from the point esceeding higher threshold with the edge point esceeding the lower threshold • Apply edge linking to fill small gaps SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  9. Edge-based segmentation • Laplacian of Gaussian zero-crossing edge detection (LOG) • The Laplacian - 2D isotropic measure of the second spatial derivative of an image • L of an image has the lagest magnitudes at peaks of intensity • L of an image has zero crossings at the points of inflection • Common convolution kernels to calculate digital Laplacian: 0 1 0 1 1 1 1 -4 1 1 -8 1 0 1 0 1 1 1 • L sensitive to noise => applied after a Gaussian smoothing filter • Hence => LOG or Marr-Hildreth operator SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  10. Explicit model-based segmentation • The Hough transform (CMEs – Bergmans) • Uses an accumulator array with dimension equal the number of parameters in the family of curves to be detected • If y = ax + b, then a and b and accumulator array indices (2) correspond • Accumulator array • Ribbon detection • Modified Hough transform which includes a directions of the intensity gradient across the line or curve SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  11. Miscelleneous methods • Image cleaning (solar: shape and intensity) • Image filtering • Image enhancement (to increase a contrast) • Morphological operations (to complete the feature shape) • Others (reported by other speakers) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  12. Artificial Neural Networks • Standard technique • Exploits a feed-forward fully connected network: input, hidden or output neurons connected by adjustable synaptic weights • The technique implies that ANN structure is well defined • It means that one must preset the input and hidden neurons • Apply suitable neuron activation function • Sigmoid activation function: y = f(x, w) = 1/(1 + exp(– w0 – Σim wi xi)), where m – number of variables x1, xm, X is the input vector, w is a synaptic weigh vector • User must choose a suitable learning algorithm • Rationally set learning rate, a number of the training epochs etc. • If ANN includes 2 hidden neurons -> back-projection algorithm provides best results SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  13. Filament recognition with ANN SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  14. Recognised filaments SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  15. Summary of the Solar Feature Recognition Methods SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  16. VII. Conclusions • WP5 is successfully implementing the project plan • Feature recognition in solar images generated a substantial interest among the IT and solar community -FR Workshop • A few novel techniques were developed for each feature (see sunspots, ARs, filaments (ANN + MO), magnetic NL) • Ongoing collaboration with the partners from Meudon, NSO, UAS, IAS and OATO • The current status – a detailed catalogue design stage SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  17. WP5 –Feature RecognitionWork in progress • Adjustment of the FR techniques to the specifics of each catalogue with respect to the time coverage period and providers for the Unified Observing Catalogues (UOC) • Created an Access database fed by the detected sunspot feature parameters and developed a preliminary query and response pages • Preparing a Demo on the Web for your testing • http://www.cyber.brad.ac.uk/egso/ SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

More Related